Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Awesome-LLM-Robotics
A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites
https://github.com/GT-RIPL/Awesome-LLM-Robotics
Last synced: 1 day ago
JSON representation
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Manipulation
- [Paper
- [Paper - grop)]
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper - fields)] [[Website](https://mahis.life/clip-fields/)]
- [Paper
- [Paper
- [Paper - us/research/group/autonomous-systems-group-robotics/articles/robot-language/)]
- [Paper
- [Paper
- [Paper
- [Paper - clip)]
- [Paper
- [Paper
- [Paper - pal-lab/LIV)] [[Website](https://penn-pal-lab.github.io/LIV/)]
- [Paper - ILIAD/lilac)]
- [Paper - grop)]
- [Paper
- [Paper
- [Paper - Planner)]
- [Paper - RL/Plan4MC)] [[Website](https://sites.google.com/view/plan4mc)]
- [Paper
- [Paper - ai-robotics/scalingup)] [[Website](https://www.cs.columbia.edu/~huy/scalingup/)]
- [Paper/PDF - generalist-agent)]
- [Paper - a-self-improving-robotic-agent)]
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper
- Text2Reward - to-reward.github.io/)]
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper - garment/home)]
- [Paper - aff)]
- [Paper
- [Paper - ma.github.io/)]
- [Paper
- [Paper
- [Paper - grop)]
- [Paper - generalist-agent)]
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Surveys
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Reasoning
- [Paper - rt.github.io/)]
- [Paper - generalist/embodied-generalist)] [[Website](https://embodied-generalist.github.io/)]
- [Paper - Embodied-AI/RoboGen)] [[Website](https://robogen-ai.github.io/)]
- [Paper
- [Paper
- [Paper
- [Paper - e.github.io/)]
- [Paper - research/robotics_transformer)] [[Website](https://robotics-transformer.github.io/)]
- [Paper
- [Paper - research/google-research/tree/master/code_as_policies)] [[Website](https://code-as-policies.github.io/)]
- [Paper - can.github.io/#open-source)] [[Website](https://say-can.github.io/)]
- [Paper
- [Paper - zhao/Matcha)] [[Website](https://matcha-model.github.io/)]
- [Paper - research/generative_agents)]
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper - learning-freiburg/MoMa-LLM)] [[Website](http://moma-llm.cs.uni-freiburg.de/)]
- [Paper - robot.github.io)]
- LLaRP - rl.github.io)]
- RT-X - transformer-x.github.io/)]
- RT-2 - transformer2.github.io/)]
- [Paper - nus/octopi)] [[Website](https://octopi-tactile-lvlm.github.io/)]
- [Paper
- [Paper - CLEAR)]
- [Paper - vlm.github.io/)]
- [Paper - robot.github.io)]
- [Paper - rt.github.io/)]
- RT-X - transformer-x.github.io/)]
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Planning
- [Paper - vila.github.io/)]
- [Paper
- [Paper
- [Paper - paper)]
- [Paper - XIX/llm-pddl)]
- [Paper
- [Paper - us/research/group/autonomous-systems-group-robotics/articles/chatgpt-for-robotics/)]
- [Paper
- [Paper - Robot-Manipulation-Prompts)]
- [Paper
- [Paper
- [Paper
- [Paper - Model-Pre-training-Improves-Generalization-in-Policy-Learning)] [[Website](https://shuangli-project.github.io/Pre-Trained-Language-Models-for-Interactive-Decision-Making/)]
- [Paper
- [Paper - lm.github.io/)]
- [Paper
- [Paper - moo.github.io/)]
- [Paper - nlp/calm-textgame)]
- [Paper
- [Paper
- [Paper - NLP-Group/LLM-Planner/)] [[Website](https://dki-lab.github.io/LLM-Planner/)]
- [Paper - decoding.github.io/)]
- [Paper - Planner)] [[Website](https://cowplanning.github.io/)]
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper
- [Paper - Foundation-Model/Co-LLM-Agents)] [[Website](https://vis-www.cs.umass.edu/Co-LLM-Agents/)]
- [Paper - to-reward.github.io/)]
- [Paper
- [Paper - eu.github.io/Loom/)][[Code](https://github.com/HRI-EU/Loom/tree/main)]
- [Paper - POLIMI/BTGenBot)]
- [Paper
- [Paper
- [Paper - eu.github.io/AttentiveSupport/)][[Code](https://github.com/HRI-EU/AttentiveSupport)]
- [Paper
- [Paper
- [Paper
- [Paper - planner)] [[Website](https://wenlong.page/language-planner/)]
- [Paper
- [Paper - TAMP)]
- [Paper
- [Paper - agent.github.io/)], [[Code](https://github.com/Kchu/LABOR-Agent)]
- [Paper - team-robotics/wonderful_team_robotics)] [[Website](https://wonderful-team-robotics.github.io/)]
- [Paper
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Instructions and Navigation
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Simulation Frameworks
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Safety, Risks, Red Teaming, and Adversarial Testing